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🧹 Clean up setup module - NBDev behind the scenes
- Focused on original purpose: just setting up development environment - Students don't see NBDev educational features explanations - Simple workflow: hello world, basic class, export, test, progress - NBDev #|hide directive works behind scenes for instructor solutions - Clean and simple, just like the original but with hidden solutions Back to basics: setup is about setup, not teaching NBDev features
This commit is contained in:
Vijay Janapa Reddi
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<section id="module-0-setup---tinytorch-development-workflow" class="level1">
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<h1>Module 0: Setup - Tiny🔥Torch Development Workflow</h1>
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<p>Welcome to TinyTorch! This module teaches you the development workflow you’ll use throughout the course.</p>
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<section id="learning-goals" class="level2">
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<h2 class="anchored" data-anchor-id="learning-goals">Learning Goals</h2>
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<ul>
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<li>Understand the nbdev notebook-to-Python workflow</li>
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<li>Write your first TinyTorch code</li>
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<li>Run tests and use the CLI tools</li>
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<li>Get comfortable with the development rhythm</li>
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</ul>
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</section>
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<section id="the-tinytorch-development-cycle" class="level2">
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<h2 class="anchored" data-anchor-id="the-tinytorch-development-cycle">The TinyTorch Development Cycle</h2>
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<ol type="1">
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<li><strong>Write code</strong> in this notebook using <code>#| export</code></li>
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<li><strong>Export code</strong> with <code>python bin/tito.py sync</code></li>
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<li><strong>Run tests</strong> with <code>python bin/tito.py test --module setup</code></li>
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<li><strong>Check progress</strong> with <code>python bin/tito.py info</code></li>
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</ol>
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<p>Let’s get started!</p>
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<p>::: {#cell-1 .cell 0=‘d’ 1=‘e’ 2=‘f’ 3=‘a’ 4=‘u’ 5=‘l’ 6=‘t’ 7=’_’ 8=‘e’ 9=‘x’ 10=‘p’ 11=’ ’ 12=‘c’ 13=‘o’ 14=‘r’ 15=‘e’ 16=‘.’ 17=‘u’ 18=‘t’ 19=‘i’ 20=‘l’ 21=‘s’}</p>
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<div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Setup imports and environment</span></span>
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<span id="cb1-2"><a href="#cb1-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sys</span>
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<span id="cb1-3"><a href="#cb1-3" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> platform</span>
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<span id="cb1-4"><a href="#cb1-4" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> datetime <span class="im">import</span> datetime</span>
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<span id="cb1-5"><a href="#cb1-5" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb1-6"><a href="#cb1-6" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="st">"🔥 TinyTorch Development Environment"</span>)</span>
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<span id="cb1-7"><a href="#cb1-7" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Python </span><span class="sc">{</span>sys<span class="sc">.</span>version<span class="sc">}</span><span class="ss">"</span>)</span>
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<span id="cb1-8"><a href="#cb1-8" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Platform: </span><span class="sc">{</span>platform<span class="sc">.</span>system()<span class="sc">}</span><span class="ss"> </span><span class="sc">{</span>platform<span class="sc">.</span>release()<span class="sc">}</span><span class="ss">"</span>)</span>
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<span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Started: </span><span class="sc">{</span>datetime<span class="sc">.</span>now()<span class="sc">.</span>strftime(<span class="st">'%Y-%m-</span><span class="sc">%d</span><span class="st"> %H:%M:%S'</span>)<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>:::</p>
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</section>
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<section id="step-1-understanding-the-module-package-structure" class="level2">
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<h2 class="anchored" data-anchor-id="step-1-understanding-the-module-package-structure">Step 1: Understanding the Module → Package Structure</h2>
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<p><strong>🎓 Teaching vs. 🔧 Building</strong>: This course has two sides: - <strong>Teaching side</strong>: You work in <code>modules/setup/setup_dev.ipynb</code> (learning-focused) - <strong>Building side</strong>: Your code exports to <code>tinytorch/core/utils.py</code> (production package)</p>
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<p><strong>Key Concept</strong>: The <code>#| default_exp core.utils</code> directive at the top tells nbdev to export all <code>#| export</code> cells to <code>tinytorch/core/utils.py</code>.</p>
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<p>This separation allows us to: - Organize learning by <strong>concepts</strong> (modules)<br>
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- Organize code by <strong>function</strong> (package structure) - Build a real ML framework while learning systematically</p>
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<p>Let’s write a simple “Hello World” function with the <code>#| export</code> directive:</p>
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<p>::: {#cell-3 .cell 0=‘e’ 1=‘x’ 2=‘p’ 3=‘o’ 4=‘r’ 5=‘t’}</p>
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<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> hello_tinytorch():</span>
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<span id="cb2-2"><a href="#cb2-2" aria-hidden="true" tabindex="-1"></a> <span class="co">"""A simple hello world function for TinyTorch."""</span></span>
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<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="st">"Hello from TinyTorch! 🔥"</span></span>
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<span id="cb2-4"><a href="#cb2-4" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb2-5"><a href="#cb2-5" aria-hidden="true" tabindex="-1"></a><span class="kw">def</span> add_numbers(a, b):</span>
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<span id="cb2-6"><a href="#cb2-6" aria-hidden="true" tabindex="-1"></a> <span class="co">"""Add two numbers together."""</span></span>
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<span id="cb2-7"><a href="#cb2-7" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> a <span class="op">+</span> b</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>:::</p>
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<div id="cell-4" class="cell">
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<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Test the functions in the notebook</span></span>
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<span id="cb3-2"><a href="#cb3-2" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(hello_tinytorch())</span>
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<span id="cb3-3"><a href="#cb3-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"2 + 3 = </span><span class="sc">{</span>add_numbers(<span class="dv">2</span>, <span class="dv">3</span>)<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
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</section>
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<section id="step-2-a-simple-class" class="level2">
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<h2 class="anchored" data-anchor-id="step-2-a-simple-class">Step 2: A Simple Class</h2>
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<p>Let’s create a simple class that will help us understand system information. This is still basic, but shows how to structure classes in TinyTorch.</p>
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<p>::: {#cell-6 .cell 0=‘e’ 1=‘x’ 2=‘p’ 3=‘o’ 4=‘r’ 5=‘t’}</p>
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<div class="sourceCode cell-code" id="cb4"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb4-1"><a href="#cb4-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> sys</span>
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<span id="cb4-2"><a href="#cb4-2" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> platform</span>
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<span id="cb4-3"><a href="#cb4-3" aria-hidden="true" tabindex="-1"></a></span>
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<span id="cb4-4"><a href="#cb4-4" aria-hidden="true" tabindex="-1"></a><span class="kw">class</span> SystemInfo:</span>
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<span id="cb4-5"><a href="#cb4-5" aria-hidden="true" tabindex="-1"></a> <span class="co">"""Simple system information class."""</span></span>
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<span id="cb4-6"><a href="#cb4-6" aria-hidden="true" tabindex="-1"></a> </span>
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<span id="cb4-7"><a href="#cb4-7" aria-hidden="true" tabindex="-1"></a> <span class="kw">def</span> <span class="fu">__init__</span>(<span class="va">self</span>):</span>
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<span id="cb4-8"><a href="#cb4-8" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.python_version <span class="op">=</span> sys.version_info</span>
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<span id="cb4-9"><a href="#cb4-9" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.platform <span class="op">=</span> platform.system()</span>
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<span id="cb4-10"><a href="#cb4-10" aria-hidden="true" tabindex="-1"></a> <span class="va">self</span>.machine <span class="op">=</span> platform.machine()</span>
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<span id="cb4-11"><a href="#cb4-11" aria-hidden="true" tabindex="-1"></a> </span>
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<span id="cb4-12"><a href="#cb4-12" aria-hidden="true" tabindex="-1"></a> <span class="kw">def</span> <span class="fu">__str__</span>(<span class="va">self</span>):</span>
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<span id="cb4-13"><a href="#cb4-13" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="ss">f"Python </span><span class="sc">{</span><span class="va">self</span><span class="sc">.</span>python_version<span class="sc">.</span>major<span class="sc">}</span><span class="ss">.</span><span class="sc">{</span><span class="va">self</span><span class="sc">.</span>python_version<span class="sc">.</span>minor<span class="sc">}</span><span class="ss"> on </span><span class="sc">{</span><span class="va">self</span><span class="sc">.</span>platform<span class="sc">}</span><span class="ss"> (</span><span class="sc">{</span><span class="va">self</span><span class="sc">.</span>machine<span class="sc">}</span><span class="ss">)"</span></span>
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<span id="cb4-14"><a href="#cb4-14" aria-hidden="true" tabindex="-1"></a> </span>
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<span id="cb4-15"><a href="#cb4-15" aria-hidden="true" tabindex="-1"></a> <span class="kw">def</span> is_compatible(<span class="va">self</span>):</span>
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<span id="cb4-16"><a href="#cb4-16" aria-hidden="true" tabindex="-1"></a> <span class="co">"""Check if system meets minimum requirements."""</span></span>
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<span id="cb4-17"><a href="#cb4-17" aria-hidden="true" tabindex="-1"></a> <span class="cf">return</span> <span class="va">self</span>.python_version <span class="op">>=</span> (<span class="dv">3</span>, <span class="dv">8</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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<p>:::</p>
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<div id="cell-7" class="cell">
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<div class="sourceCode cell-code" id="cb5"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb5-1"><a href="#cb5-1" aria-hidden="true" tabindex="-1"></a><span class="co"># Test the SystemInfo class</span></span>
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<span id="cb5-2"><a href="#cb5-2" aria-hidden="true" tabindex="-1"></a>info <span class="op">=</span> SystemInfo()</span>
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<span id="cb5-3"><a href="#cb5-3" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"System: </span><span class="sc">{</span>info<span class="sc">}</span><span class="ss">"</span>)</span>
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<span id="cb5-4"><a href="#cb5-4" aria-hidden="true" tabindex="-1"></a><span class="bu">print</span>(<span class="ss">f"Compatible: </span><span class="sc">{</span>info<span class="sc">.</span>is_compatible()<span class="sc">}</span><span class="ss">"</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
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</div>
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</section>
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<section id="step-3-try-the-export-process" class="level2">
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<h2 class="anchored" data-anchor-id="step-3-try-the-export-process">Step 3: Try the Export Process</h2>
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<p>Now let’s export our code! In your terminal, run:</p>
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<div class="sourceCode" id="cb6"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb6-1"><a href="#cb6-1" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> bin/tito.py sync <span class="at">--module</span> setup</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
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<p>This will export the code marked with <code>#| export</code> to <code>tinytorch/core/utils.py</code>.</p>
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<p><strong>What happens during export:</strong> 1. nbdev scans this notebook for <code>#| export</code> cells 2. Extracts the Python code<br>
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3. Writes it to <code>tinytorch/core/utils.py</code> (because of <code>#| default_exp core.utils</code>) 4. Handles imports and dependencies automatically</p>
|
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<p><strong>🔍 Verification</strong>: After export, check <code>tinytorch/core/utils.py</code> - you’ll see your functions there with auto-generated headers pointing back to this notebook!</p>
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</section>
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<section id="step-4-run-tests" class="level2">
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<h2 class="anchored" data-anchor-id="step-4-run-tests">Step 4: Run Tests</h2>
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<p>After exporting, run the tests:</p>
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<div class="sourceCode" id="cb7"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb7-1"><a href="#cb7-1" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> bin/tito.py test <span class="at">--module</span> setup</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<p>This will run all tests for the setup module and verify your implementation works correctly.</p>
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</section>
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<section id="step-5-check-your-progress" class="level2">
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<h2 class="anchored" data-anchor-id="step-5-check-your-progress">Step 5: Check Your Progress</h2>
|
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<p>See your overall progress:</p>
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<div class="sourceCode" id="cb8"><pre class="sourceCode bash code-with-copy"><code class="sourceCode bash"><span id="cb8-1"><a href="#cb8-1" aria-hidden="true" tabindex="-1"></a><span class="ex">python</span> bin/tito.py info</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
|
||||
<p>This shows which modules are complete and which are pending.</p>
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</section>
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<section id="congratulations" class="level2">
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<h2 class="anchored" data-anchor-id="congratulations">🎉 Congratulations!</h2>
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<p>You’ve learned the TinyTorch development workflow:</p>
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<ol type="1">
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<li>✅ Write code in notebooks with <code>#| export</code></li>
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<li>✅ Export with <code>tito sync</code><br>
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</li>
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<li>✅ Test with <code>tito test --module setup</code></li>
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<li>✅ Check progress with <code>tito info</code></li>
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</ol>
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<p><strong>This is the rhythm you’ll use for every module in TinyTorch.</strong></p>
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<section id="next-steps" class="level3">
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<h3 class="anchored" data-anchor-id="next-steps">Next Steps</h3>
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<p>Ready for the real work? Head to <strong>Module 1: Tensor</strong> where you’ll build the core data structures that power everything else in TinyTorch.</p>
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<p><strong>Development Tips:</strong> - Always test your code in the notebook first - Export frequently to catch issues early<br>
|
||||
- Read error messages carefully - they’re designed to help - When stuck, check if your code exports cleanly first</p>
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<p>Happy building! 🔥</p>
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</section>
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</section>
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</section>
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</main>
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<!-- /main column -->
|
||||
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throttle(() => {
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unselectCodeLines();
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selectCodeLines(clickedEl);
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el,
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cites: cites.split(' ')
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var popup = window.document.createElement('div');
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var citeDiv = window.document.createElement('div');
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citeDiv.classList.add('hanging-indent');
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citeDiv.classList.add('csl-entry');
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var biblioDiv = window.document.getElementById('ref-' + cite);
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if (biblioDiv) {
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citeDiv.innerHTML = biblioDiv.innerHTML;
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}
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}
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});
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</script>
|
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</div> <!-- /content -->
|
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|
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|
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|
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|
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</body></html>
|
||||
@@ -2,7 +2,7 @@
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "4f8e5d54",
|
||||
"id": "7dcaa167",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\""
|
||||
},
|
||||
@@ -11,23 +11,15 @@
|
||||
"\n",
|
||||
"Welcome to TinyTorch! This module teaches you the development workflow you'll use throughout the course.\n",
|
||||
"\n",
|
||||
"> **📚 Educational Mode**: This notebook uses NBDev's educational features. Instructors see complete solutions, students see exercises with hidden answers.\n",
|
||||
"\n",
|
||||
"## Learning Goals\n",
|
||||
"- Understand the NBDev notebook-to-Python workflow with educational directives\n",
|
||||
"- Write your first TinyTorch code with instructor/student mode support\n",
|
||||
"- Master the hide/show pattern for progressive learning\n",
|
||||
"- Understand the nbdev notebook-to-Python workflow\n",
|
||||
"- Write your first TinyTorch code\n",
|
||||
"- Run tests and use the CLI tools\n",
|
||||
"- Get comfortable with the development rhythm\n",
|
||||
"\n",
|
||||
"## ✨ NBDev Educational Features\n",
|
||||
"- **`#|hide`** - Hide complete solutions from students (click to reveal)\n",
|
||||
"- **`#|code-fold`** - Collapsible code sections for optional details\n",
|
||||
"- **Single source** - One notebook serves both instructors and students\n",
|
||||
"\n",
|
||||
"## The TinyTorch Development Cycle\n",
|
||||
"\n",
|
||||
"1. **Write code** in this notebook using `#| export` and educational directives\n",
|
||||
"1. **Write code** in this notebook using `#| export` \n",
|
||||
"2. **Export code** with `python bin/tito.py sync --module setup`\n",
|
||||
"3. **Run tests** with `python bin/tito.py test --module setup`\n",
|
||||
"4. **Check progress** with `python bin/tito.py info`\n",
|
||||
@@ -38,7 +30,7 @@
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "a8106015",
|
||||
"id": "70bc17f4",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
@@ -49,7 +41,7 @@
|
||||
"import platform\n",
|
||||
"from datetime import datetime\n",
|
||||
"\n",
|
||||
"print(\"🔥 TinyTorch Development Environment - Educational Mode\")\n",
|
||||
"print(\"🔥 TinyTorch Development Environment\")\n",
|
||||
"print(f\"Python {sys.version}\")\n",
|
||||
"print(f\"Platform: {platform.system()} {platform.release()}\")\n",
|
||||
"print(f\"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\")"
|
||||
@@ -57,7 +49,7 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c1919f0e",
|
||||
"id": "d476652e",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\"",
|
||||
"lines_to_next_cell": 1
|
||||
@@ -76,15 +68,13 @@
|
||||
"- Organize code by **function** (package structure)\n",
|
||||
"- Build a real ML framework while learning systematically\n",
|
||||
"\n",
|
||||
"### 🎯 Your First Challenge: Educational Hello World\n",
|
||||
"\n",
|
||||
"Let's implement a hello world function that teaches ML concepts. **Students**: Try implementing this yourself first!"
|
||||
"Let's write a simple \"Hello World\" function with the `#| export` directive:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c7117f42",
|
||||
"id": "ce956cf5",
|
||||
"metadata": {
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
@@ -93,39 +83,21 @@
|
||||
"#| export\n",
|
||||
"def hello_tinytorch():\n",
|
||||
" \"\"\"\n",
|
||||
" A hello world function that introduces TinyTorch concepts.\n",
|
||||
" A simple hello world function for TinyTorch.\n",
|
||||
" \n",
|
||||
" Students: Implement a function that returns a welcoming message \n",
|
||||
" mentioning tensors, autograd, and neural networks.\n",
|
||||
" \n",
|
||||
" Hint: Make it inspiring and educational!\n",
|
||||
" TODO: Make this return a more welcoming message about TinyTorch.\n",
|
||||
" \"\"\"\n",
|
||||
" # TODO: Replace this placeholder with an educational greeting\n",
|
||||
" # TODO: Mention what students will build (tensors, autograd, networks)\n",
|
||||
" return \"Hello from TinyTorch! 🔥\"\n",
|
||||
"\n",
|
||||
"def add_numbers(a, b):\n",
|
||||
" \"\"\"Add two numbers - the foundation of all ML operations!\"\"\"\n",
|
||||
" \"\"\"Add two numbers together.\"\"\"\n",
|
||||
" return a + b"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "67809246",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\"",
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
"source": [
|
||||
"### 🔍 Instructor Solution (Hidden from Students)\n",
|
||||
"\n",
|
||||
"Click the cell below to see the complete educational implementation:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8a7836bc",
|
||||
"id": "da7b047a",
|
||||
"metadata": {
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
@@ -134,143 +106,39 @@
|
||||
"#| hide\n",
|
||||
"#| export\n",
|
||||
"def hello_tinytorch():\n",
|
||||
" \"\"\"INSTRUCTOR SOLUTION: A comprehensive TinyTorch introduction.\"\"\"\n",
|
||||
" return \"\"\"🔥 Welcome to TinyTorch - Your ML Systems Journey! 🔥\n",
|
||||
"\n",
|
||||
"What you'll build in this course:\n",
|
||||
"📊 Tensors: N-dimensional arrays for data\n",
|
||||
"🔄 Autograd: Automatic differentiation engine \n",
|
||||
"🧠 Neural Networks: MLPs, CNNs, and more\n",
|
||||
"⚡ Training: Optimizers, loss functions, loops\n",
|
||||
"🚀 Production: Deployment and monitoring\n",
|
||||
"\n",
|
||||
"You're not just learning ML - you're building a complete framework from scratch!\n",
|
||||
"Ready to become an ML systems engineer? Let's go! 💪\"\"\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "7db283f8",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\""
|
||||
},
|
||||
"source": [
|
||||
"### 🧪 Test Your Implementation\n",
|
||||
"\n",
|
||||
"Run the cell below to test your hello world function:"
|
||||
" \"\"\"A simple hello world function for TinyTorch.\"\"\"\n",
|
||||
" return \"🔥 Welcome to TinyTorch! Ready to build ML systems from scratch? Let's go! 🔥\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3362bc6a",
|
||||
"id": "ffa17648",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test the functions in the notebook\n",
|
||||
"print(\"=== Testing Hello World Function ===\")\n",
|
||||
"print(hello_tinytorch())\n",
|
||||
"print()\n",
|
||||
"print(\"=== Testing Basic Operations ===\")\n",
|
||||
"print(f\"2 + 3 = {add_numbers(2, 3)}\")\n",
|
||||
"print(f\"This is the foundation of neural network math!\")"
|
||||
"print(f\"2 + 3 = {add_numbers(2, 3)}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1a78ebd8",
|
||||
"id": "98dd35dc",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\"",
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
"source": [
|
||||
"### 🎯 Advanced Challenge: Vector Operations\n",
|
||||
"## Step 2: A Simple Class\n",
|
||||
"\n",
|
||||
"Let's implement something more ML-relevant. Can you implement vector operations that are fundamental to ML?"
|
||||
"Let's create a simple class that will help us understand system information. This is still basic, but shows how to structure classes in TinyTorch."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "42214640",
|
||||
"metadata": {
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#| export\n",
|
||||
"#| code-fold: true\n",
|
||||
"def vector_add(v1, v2):\n",
|
||||
" \"\"\"\n",
|
||||
" Add two vectors element-wise.\n",
|
||||
" \n",
|
||||
" Students: Implement vector addition\n",
|
||||
" - Check that vectors have same length\n",
|
||||
" - Add corresponding elements \n",
|
||||
" - Return new vector\n",
|
||||
" \n",
|
||||
" This is fundamental to ML: gradient updates, combining embeddings, etc.\n",
|
||||
" \"\"\"\n",
|
||||
" # TODO: Implement vector addition\n",
|
||||
" # Hint: Use zip() to pair up elements\n",
|
||||
" if len(v1) != len(v2):\n",
|
||||
" raise ValueError(f\"Vector lengths don't match: {len(v1)} vs {len(v2)}\")\n",
|
||||
" \n",
|
||||
" return [a + b for a, b in zip(v1, v2)]\n",
|
||||
"\n",
|
||||
"def vector_dot(v1, v2):\n",
|
||||
" \"\"\"\n",
|
||||
" Compute dot product of two vectors.\n",
|
||||
" \n",
|
||||
" Students: Implement dot product\n",
|
||||
" - Multiply corresponding elements\n",
|
||||
" - Sum the results\n",
|
||||
" \n",
|
||||
" Dot product is THE core ML operation (linear layers, attention, etc.)\n",
|
||||
" \"\"\"\n",
|
||||
" # TODO: Implement dot product\n",
|
||||
" if len(v1) != len(v2):\n",
|
||||
" raise ValueError(f\"Vector lengths don't match: {len(v1)} vs {len(v2)}\")\n",
|
||||
" \n",
|
||||
" return sum(a * b for a, b in zip(v1, v2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2c3abc49",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test vector operations\n",
|
||||
"print(\"=== Vector Operations Test ===\")\n",
|
||||
"v1 = [1.0, 2.0, 3.0]\n",
|
||||
"v2 = [4.0, 5.0, 6.0]\n",
|
||||
"\n",
|
||||
"print(f\"Vector 1: {v1}\")\n",
|
||||
"print(f\"Vector 2: {v2}\")\n",
|
||||
"print(f\"Addition: {vector_add(v1, v2)}\")\n",
|
||||
"print(f\"Dot product: {vector_dot(v1, v2)}\")\n",
|
||||
"print(\"These operations power all of machine learning!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "90407e30",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\"",
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
"source": [
|
||||
"## Step 2: ML-Aware System Information\n",
|
||||
"\n",
|
||||
"Let's create a more sophisticated system class that's ML-aware. This demonstrates object-oriented programming while providing useful ML development information."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d1754aeb",
|
||||
"id": "8adcd70a",
|
||||
"metadata": {
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
@@ -278,226 +146,42 @@
|
||||
"source": [
|
||||
"#| export\n",
|
||||
"class SystemInfo:\n",
|
||||
" \"\"\"ML-aware system information class.\"\"\"\n",
|
||||
" \"\"\"Simple system information class.\"\"\"\n",
|
||||
" \n",
|
||||
" def __init__(self):\n",
|
||||
" \"\"\"Initialize system information collection.\"\"\"\n",
|
||||
" self.python_version = sys.version_info\n",
|
||||
" self.platform = platform.system()\n",
|
||||
" self.machine = platform.machine()\n",
|
||||
" self._check_ml_libraries()\n",
|
||||
" \n",
|
||||
" def _check_ml_libraries(self):\n",
|
||||
" \"\"\"Check if common ML libraries are available.\"\"\"\n",
|
||||
" self.has_numpy = self._try_import('numpy')\n",
|
||||
" self.has_torch = self._try_import('torch')\n",
|
||||
" self.has_tensorflow = self._try_import('tensorflow')\n",
|
||||
" \n",
|
||||
" def _try_import(self, module_name):\n",
|
||||
" \"\"\"Safely try to import a module.\"\"\"\n",
|
||||
" try:\n",
|
||||
" __import__(module_name)\n",
|
||||
" return True\n",
|
||||
" except ImportError:\n",
|
||||
" return False\n",
|
||||
" \n",
|
||||
" def __str__(self):\n",
|
||||
" \"\"\"Human-readable system information.\"\"\"\n",
|
||||
" return f\"TinyTorch on Python {self.python_version.major}.{self.python_version.minor} ({self.platform} {self.machine})\"\n",
|
||||
" return f\"Python {self.python_version.major}.{self.python_version.minor} on {self.platform} ({self.machine})\"\n",
|
||||
" \n",
|
||||
" def is_compatible(self):\n",
|
||||
" \"\"\"Check if system meets minimum requirements.\"\"\"\n",
|
||||
" return self.python_version >= (3, 8)\n",
|
||||
" \n",
|
||||
" def is_ml_ready(self):\n",
|
||||
" \"\"\"Check if system is ready for ML development.\"\"\"\n",
|
||||
" return self.is_compatible() and self.has_numpy\n",
|
||||
" \n",
|
||||
" def ml_status_report(self):\n",
|
||||
" \"\"\"Generate a detailed ML readiness report.\"\"\"\n",
|
||||
" status = []\n",
|
||||
" status.append(\"🔥 TinyTorch System Status\")\n",
|
||||
" status.append(f\"Platform: {self.platform} ({self.machine})\")\n",
|
||||
" status.append(f\"Python: {self.python_version.major}.{self.python_version.minor}.{self.python_version.micro}\")\n",
|
||||
" \n",
|
||||
" # Check requirements\n",
|
||||
" status.append(\"\\n📋 ML Library Status:\")\n",
|
||||
" status.append(f\" NumPy: {'✅ Available' if self.has_numpy else '❌ Missing'}\")\n",
|
||||
" status.append(f\" PyTorch: {'✅ Available' if self.has_torch else '❌ Missing (optional)'}\")\n",
|
||||
" status.append(f\" TensorFlow: {'✅ Available' if self.has_tensorflow else '❌ Missing (optional)'}\")\n",
|
||||
" \n",
|
||||
" # Overall status\n",
|
||||
" ready = self.is_ml_ready()\n",
|
||||
" status.append(f\"\\n🎯 Overall Status: {'✅ Ready for TinyTorch!' if ready else '❌ Missing requirements'}\")\n",
|
||||
" \n",
|
||||
" if ready:\n",
|
||||
" status.append(\"🚀 You're all set to build an ML framework from scratch!\")\n",
|
||||
" else:\n",
|
||||
" status.append(\"💡 Install missing libraries: pip install numpy\")\n",
|
||||
" \n",
|
||||
" return \"\\n\".join(status)"
|
||||
" return self.python_version >= (3, 8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "25ad5b93",
|
||||
"id": "28f33bb2",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Test the enhanced SystemInfo class\n",
|
||||
"print(\"=== Enhanced System Information ===\")\n",
|
||||
"# Test the SystemInfo class\n",
|
||||
"info = SystemInfo()\n",
|
||||
"print(f\"System: {info}\")\n",
|
||||
"print(f\"Compatible: {info.is_compatible()}\")\n",
|
||||
"print(f\"ML Ready: {info.is_ml_ready()}\")\n",
|
||||
"print()\n",
|
||||
"print(info.ml_status_report())"
|
||||
"print(f\"Compatible: {info.is_compatible()}\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "e112b053",
|
||||
"id": "2eae1d7f",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\""
|
||||
},
|
||||
"source": [
|
||||
"## Step 3: The NBDev Export Process - Your Educational Superpower\n",
|
||||
"\n",
|
||||
"Now let's understand how NBDev transforms your notebook into production code while maintaining the educational experience!\n",
|
||||
"\n",
|
||||
"### 🔄 The Magic of Educational `#| export`\n",
|
||||
"\n",
|
||||
"Every cell marked with `#| export` becomes part of the `tinytorch` package, but NBDev's educational directives control what students see vs what instructors see."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "784fd672",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#| code-fold: show\n",
|
||||
"print(\"=== NBDev Educational Export Demonstration ===\")\n",
|
||||
"print(\"🎓 Learning Side: You work in modules/setup/setup_dev.ipynb\")\n",
|
||||
"print(\"🔧 Building Side: Code exports to tinytorch/core/utils.py\")\n",
|
||||
"print()\n",
|
||||
"print(\"✨ Educational Directives Used:\")\n",
|
||||
"print(\" #|export - Code goes to package\")\n",
|
||||
"print(\" #|hide - Solutions hidden from students\") \n",
|
||||
"print(\" #|code-fold - Collapsible sections\")\n",
|
||||
"print(\" Single source - One notebook, two audiences\")\n",
|
||||
"print()\n",
|
||||
"print(\"🚀 Try this export command:\")\n",
|
||||
"print(\" python bin/tito.py sync --module setup\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "d5ab3fcb",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\"",
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
"source": [
|
||||
"### 🎯 Advanced ML Preview (Instructor Level)\n",
|
||||
"\n",
|
||||
"The following cell demonstrates advanced concepts that will be hidden from beginners but visible to instructors:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f36eb09d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#| hide\n",
|
||||
"#| filter_stream ImportWarning DeprecationWarning\n",
|
||||
"def advanced_ml_preview():\n",
|
||||
" \"\"\"\n",
|
||||
" ADVANCED CONTENT - Hidden from beginners\n",
|
||||
" \n",
|
||||
" This demonstrates how to hide complex implementations\n",
|
||||
" while still teaching the concepts progressively.\n",
|
||||
" \"\"\"\n",
|
||||
" import math\n",
|
||||
" \n",
|
||||
" def sigmoid(x):\n",
|
||||
" \"\"\"Sigmoid activation function - fundamental to neural networks.\"\"\"\n",
|
||||
" return 1 / (1 + math.exp(-x))\n",
|
||||
" \n",
|
||||
" def relu(x):\n",
|
||||
" \"\"\"ReLU activation function - most common in modern ML.\"\"\"\n",
|
||||
" return max(0, x)\n",
|
||||
" \n",
|
||||
" # Demonstrate activation functions\n",
|
||||
" test_vals = [-2, -1, 0, 1, 2]\n",
|
||||
" print(\"🔬 Activation Functions Preview:\")\n",
|
||||
" for x in test_vals:\n",
|
||||
" print(f\" x={x:2}: sigmoid={sigmoid(x):.3f}, relu={relu(x):.3f}\")\n",
|
||||
"\n",
|
||||
"# Show preview to instructors\n",
|
||||
"print(\"🔬 Advanced ML Preview (hidden from beginners):\")\n",
|
||||
"advanced_ml_preview()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "26100d72",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\"",
|
||||
"lines_to_next_cell": 1
|
||||
},
|
||||
"source": [
|
||||
"## Step 4: Testing and Quality Assurance\n",
|
||||
"\n",
|
||||
"Quality code is essential for ML systems. Let's test our implementations!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8e871650",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def run_setup_tests():\n",
|
||||
" \"\"\"Run comprehensive tests on our setup module functions.\"\"\"\n",
|
||||
" print(\"=== Running Setup Module Tests ===\")\n",
|
||||
" \n",
|
||||
" # Test basic functions\n",
|
||||
" assert hello_tinytorch() is not None, \"hello_tinytorch should return something\"\n",
|
||||
" assert len(hello_tinytorch()) > 20, \"hello_tinytorch should be educational\"\n",
|
||||
" assert add_numbers(2, 3) == 5, \"Addition should work correctly\"\n",
|
||||
" \n",
|
||||
" # Test vector operations\n",
|
||||
" v1, v2 = [1.0, 2.0], [3.0, 4.0]\n",
|
||||
" assert vector_add(v1, v2) == [4.0, 6.0], \"Vector addition should work\"\n",
|
||||
" assert vector_dot(v1, v2) == 11.0, \"Dot product should work\"\n",
|
||||
" \n",
|
||||
" # Test system info\n",
|
||||
" info = SystemInfo()\n",
|
||||
" assert isinstance(info.ml_status_report(), str), \"Status report should be string\"\n",
|
||||
" assert info.is_compatible(), \"Should be compatible with Python 3.8+\"\n",
|
||||
" \n",
|
||||
" print(\"✅ All tests passed! Your setup module is working correctly.\")\n",
|
||||
" print(\"📚 Ready for production ML systems development!\")\n",
|
||||
" return True\n",
|
||||
"\n",
|
||||
"# Run the comprehensive tests\n",
|
||||
"run_setup_tests()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "485d6e37",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\""
|
||||
},
|
||||
"source": [
|
||||
"## Step 5: Export and Build Process\n",
|
||||
"## Step 3: Try the Export Process\n",
|
||||
"\n",
|
||||
"Now let's export our code! In your terminal, run:\n",
|
||||
"\n",
|
||||
@@ -507,24 +191,23 @@
|
||||
"\n",
|
||||
"This will export the code marked with `#| export` to `tinytorch/core/utils.py`.\n",
|
||||
"\n",
|
||||
"**What happens during educational export:**\n",
|
||||
"1. NBDev scans this notebook for `#| export` cells\n",
|
||||
"2. **Students see**: Exercise versions with TODOs and hints\n",
|
||||
"3. **Instructors see**: Complete solutions with `#|hide` directive\n",
|
||||
"4. **Package gets**: The complete implementation (instructor version)\n",
|
||||
"5. **Documentation shows**: Educational progression with hide/show buttons\n",
|
||||
"**What happens during export:**\n",
|
||||
"1. nbdev scans this notebook for `#| export` cells\n",
|
||||
"2. Extracts the Python code \n",
|
||||
"3. Writes it to `tinytorch/core/utils.py` (because of `#| default_exp core.utils`)\n",
|
||||
"4. Handles imports and dependencies automatically\n",
|
||||
"\n",
|
||||
"**🔍 Verification**: After export, check `tinytorch/core/utils.py` - you'll see the complete functions with auto-generated headers!"
|
||||
"**🔍 Verification**: After export, check `tinytorch/core/utils.py` - you'll see your functions there with auto-generated headers pointing back to this notebook!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "9811f282",
|
||||
"id": "881a8525",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\""
|
||||
},
|
||||
"source": [
|
||||
"## Step 6: Run Tests\n",
|
||||
"## Step 4: Run Tests\n",
|
||||
"\n",
|
||||
"After exporting, run the tests:\n",
|
||||
"\n",
|
||||
@@ -534,7 +217,7 @@
|
||||
"\n",
|
||||
"This will run all tests for the setup module and verify your implementation works correctly.\n",
|
||||
"\n",
|
||||
"## Step 7: Check Your Progress\n",
|
||||
"## Step 5: Check Your Progress\n",
|
||||
"\n",
|
||||
"See your overall progress:\n",
|
||||
"\n",
|
||||
@@ -547,49 +230,33 @@
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "22686369",
|
||||
"id": "7a8262c8",
|
||||
"metadata": {
|
||||
"cell_marker": "\"\"\""
|
||||
},
|
||||
"source": [
|
||||
"## 🎉 Congratulations! You've Mastered NBDev Educational Features\n",
|
||||
"## 🎉 Congratulations!\n",
|
||||
"\n",
|
||||
"### ✨ What You've Accomplished\n",
|
||||
"You've learned the TinyTorch development workflow:\n",
|
||||
"\n",
|
||||
"- ✅ **NBDev Educational Directives**: Used `#|hide`, `#|code-fold`, `#|filter_stream`\n",
|
||||
"- ✅ **Progressive Learning**: Started simple, built to ML complexity \n",
|
||||
"- ✅ **Single Source Truth**: One notebook serves students AND instructors\n",
|
||||
"- ✅ **ML-Relevant Code**: Vector operations, activation previews, system checks\n",
|
||||
"- ✅ **Quality Assurance**: Comprehensive testing approach\n",
|
||||
"- ✅ **Production Workflow**: Export to package with educational metadata\n",
|
||||
"1. ✅ Write code in notebooks with `#| export`\n",
|
||||
"2. ✅ Export with `tito sync --module setup` \n",
|
||||
"3. ✅ Test with `tito test --module setup`\n",
|
||||
"4. ✅ Check progress with `tito info`\n",
|
||||
"\n",
|
||||
"### 🔄 The Educational TinyTorch Development Rhythm\n",
|
||||
"**This is the rhythm you'll use for every module in TinyTorch.**\n",
|
||||
"\n",
|
||||
"1. **Write** code with educational directives (`#|hide` for solutions)\n",
|
||||
"2. **Test** implementations in notebook (both student and instructor versions)\n",
|
||||
"3. **Export** with `python bin/tito.py sync --module setup`\n",
|
||||
"4. **Verify** with `python bin/tito.py test --module setup`\n",
|
||||
"5. **Progress** with `python bin/tito.py info`\n",
|
||||
"### Next Steps\n",
|
||||
"\n",
|
||||
"### 🚀 Next Steps: Ready for Real ML Systems\n",
|
||||
"Ready for the real work? Head to **Module 1: Tensor** where you'll build the core data structures that power everything else in TinyTorch.\n",
|
||||
"\n",
|
||||
"You're now ready for **Module 1: Tensor** where you'll build the foundation of all ML systems using this same educational pattern!\n",
|
||||
"**Development Tips:**\n",
|
||||
"- Always test your code in the notebook first\n",
|
||||
"- Export frequently to catch issues early \n",
|
||||
"- Read error messages carefully - they're designed to help\n",
|
||||
"- When stuck, check if your code exports cleanly first\n",
|
||||
"\n",
|
||||
"**What's Coming:**\n",
|
||||
"- 📊 **Tensors**: N-dimensional arrays with educational progression\n",
|
||||
"- 🔄 **Autograd**: Automatic differentiation with hidden complexity\n",
|
||||
"- 🧠 **Networks**: MLPs, CNNs with step-by-step revelation\n",
|
||||
"- ⚡ **Training**: End-to-end learning with instructor solutions\n",
|
||||
"\n",
|
||||
"### 💡 Educational Development Tips\n",
|
||||
"\n",
|
||||
"- **Use `#|hide` strategically** - provide complete solutions but let students try first\n",
|
||||
"- **Progressive revelation** - start simple, build complexity with fold/hide\n",
|
||||
"- **Test both versions** - ensure student stubs and instructor solutions work\n",
|
||||
"- **Single source truth** - maintain one notebook, serve two audiences\n",
|
||||
"- **Quality first** - educational code should be production-ready\n",
|
||||
"\n",
|
||||
"**Happy building, ML educator! 🔥**"
|
||||
"Happy building! 🔥"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
||||
@@ -14,23 +14,15 @@
|
||||
|
||||
Welcome to TinyTorch! This module teaches you the development workflow you'll use throughout the course.
|
||||
|
||||
> **📚 Educational Mode**: This notebook uses NBDev's educational features. Instructors see complete solutions, students see exercises with hidden answers.
|
||||
|
||||
## Learning Goals
|
||||
- Understand the NBDev notebook-to-Python workflow with educational directives
|
||||
- Write your first TinyTorch code with instructor/student mode support
|
||||
- Master the hide/show pattern for progressive learning
|
||||
- Understand the nbdev notebook-to-Python workflow
|
||||
- Write your first TinyTorch code
|
||||
- Run tests and use the CLI tools
|
||||
- Get comfortable with the development rhythm
|
||||
|
||||
## ✨ NBDev Educational Features
|
||||
- **`#|hide`** - Hide complete solutions from students (click to reveal)
|
||||
- **`#|code-fold`** - Collapsible code sections for optional details
|
||||
- **Single source** - One notebook serves both instructors and students
|
||||
|
||||
## The TinyTorch Development Cycle
|
||||
|
||||
1. **Write code** in this notebook using `#| export` and educational directives
|
||||
1. **Write code** in this notebook using `#| export`
|
||||
2. **Export code** with `python bin/tito.py sync --module setup`
|
||||
3. **Run tests** with `python bin/tito.py test --module setup`
|
||||
4. **Check progress** with `python bin/tito.py info`
|
||||
@@ -46,7 +38,7 @@ import sys
|
||||
import platform
|
||||
from datetime import datetime
|
||||
|
||||
print("🔥 TinyTorch Development Environment - Educational Mode")
|
||||
print("🔥 TinyTorch Development Environment")
|
||||
print(f"Python {sys.version}")
|
||||
print(f"Platform: {platform.system()} {platform.release()}")
|
||||
print(f"Started: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}")
|
||||
@@ -66,305 +58,68 @@ This separation allows us to:
|
||||
- Organize code by **function** (package structure)
|
||||
- Build a real ML framework while learning systematically
|
||||
|
||||
### 🎯 Your First Challenge: Educational Hello World
|
||||
|
||||
Let's implement a hello world function that teaches ML concepts. **Students**: Try implementing this yourself first!
|
||||
Let's write a simple "Hello World" function with the `#| export` directive:
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
def hello_tinytorch():
|
||||
"""
|
||||
A hello world function that introduces TinyTorch concepts.
|
||||
A simple hello world function for TinyTorch.
|
||||
|
||||
Students: Implement a function that returns a welcoming message
|
||||
mentioning tensors, autograd, and neural networks.
|
||||
|
||||
Hint: Make it inspiring and educational!
|
||||
TODO: Make this return a more welcoming message about TinyTorch.
|
||||
"""
|
||||
# TODO: Replace this placeholder with an educational greeting
|
||||
# TODO: Mention what students will build (tensors, autograd, networks)
|
||||
return "Hello from TinyTorch! 🔥"
|
||||
|
||||
def add_numbers(a, b):
|
||||
"""Add two numbers - the foundation of all ML operations!"""
|
||||
"""Add two numbers together."""
|
||||
return a + b
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🔍 Instructor Solution (Hidden from Students)
|
||||
|
||||
Click the cell below to see the complete educational implementation:
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| hide
|
||||
#| export
|
||||
def hello_tinytorch():
|
||||
"""INSTRUCTOR SOLUTION: A comprehensive TinyTorch introduction."""
|
||||
return """🔥 Welcome to TinyTorch - Your ML Systems Journey! 🔥
|
||||
|
||||
What you'll build in this course:
|
||||
📊 Tensors: N-dimensional arrays for data
|
||||
🔄 Autograd: Automatic differentiation engine
|
||||
🧠 Neural Networks: MLPs, CNNs, and more
|
||||
⚡ Training: Optimizers, loss functions, loops
|
||||
🚀 Production: Deployment and monitoring
|
||||
|
||||
You're not just learning ML - you're building a complete framework from scratch!
|
||||
Ready to become an ML systems engineer? Let's go! 💪"""
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🧪 Test Your Implementation
|
||||
|
||||
Run the cell below to test your hello world function:
|
||||
"""
|
||||
"""A simple hello world function for TinyTorch."""
|
||||
return "🔥 Welcome to TinyTorch! Ready to build ML systems from scratch? Let's go! 🔥"
|
||||
|
||||
# %%
|
||||
# Test the functions in the notebook
|
||||
print("=== Testing Hello World Function ===")
|
||||
print(hello_tinytorch())
|
||||
print()
|
||||
print("=== Testing Basic Operations ===")
|
||||
print(f"2 + 3 = {add_numbers(2, 3)}")
|
||||
print(f"This is the foundation of neural network math!")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🎯 Advanced Challenge: Vector Operations
|
||||
## Step 2: A Simple Class
|
||||
|
||||
Let's implement something more ML-relevant. Can you implement vector operations that are fundamental to ML?
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
#| code-fold: true
|
||||
def vector_add(v1, v2):
|
||||
"""
|
||||
Add two vectors element-wise.
|
||||
|
||||
Students: Implement vector addition
|
||||
- Check that vectors have same length
|
||||
- Add corresponding elements
|
||||
- Return new vector
|
||||
|
||||
This is fundamental to ML: gradient updates, combining embeddings, etc.
|
||||
"""
|
||||
# TODO: Implement vector addition
|
||||
# Hint: Use zip() to pair up elements
|
||||
if len(v1) != len(v2):
|
||||
raise ValueError(f"Vector lengths don't match: {len(v1)} vs {len(v2)}")
|
||||
|
||||
return [a + b for a, b in zip(v1, v2)]
|
||||
|
||||
def vector_dot(v1, v2):
|
||||
"""
|
||||
Compute dot product of two vectors.
|
||||
|
||||
Students: Implement dot product
|
||||
- Multiply corresponding elements
|
||||
- Sum the results
|
||||
|
||||
Dot product is THE core ML operation (linear layers, attention, etc.)
|
||||
"""
|
||||
# TODO: Implement dot product
|
||||
if len(v1) != len(v2):
|
||||
raise ValueError(f"Vector lengths don't match: {len(v1)} vs {len(v2)}")
|
||||
|
||||
return sum(a * b for a, b in zip(v1, v2))
|
||||
|
||||
# %%
|
||||
# Test vector operations
|
||||
print("=== Vector Operations Test ===")
|
||||
v1 = [1.0, 2.0, 3.0]
|
||||
v2 = [4.0, 5.0, 6.0]
|
||||
|
||||
print(f"Vector 1: {v1}")
|
||||
print(f"Vector 2: {v2}")
|
||||
print(f"Addition: {vector_add(v1, v2)}")
|
||||
print(f"Dot product: {vector_dot(v1, v2)}")
|
||||
print("These operations power all of machine learning!")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 2: ML-Aware System Information
|
||||
|
||||
Let's create a more sophisticated system class that's ML-aware. This demonstrates object-oriented programming while providing useful ML development information.
|
||||
Let's create a simple class that will help us understand system information. This is still basic, but shows how to structure classes in TinyTorch.
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| export
|
||||
class SystemInfo:
|
||||
"""ML-aware system information class."""
|
||||
"""Simple system information class."""
|
||||
|
||||
def __init__(self):
|
||||
"""Initialize system information collection."""
|
||||
self.python_version = sys.version_info
|
||||
self.platform = platform.system()
|
||||
self.machine = platform.machine()
|
||||
self._check_ml_libraries()
|
||||
|
||||
def _check_ml_libraries(self):
|
||||
"""Check if common ML libraries are available."""
|
||||
self.has_numpy = self._try_import('numpy')
|
||||
self.has_torch = self._try_import('torch')
|
||||
self.has_tensorflow = self._try_import('tensorflow')
|
||||
|
||||
def _try_import(self, module_name):
|
||||
"""Safely try to import a module."""
|
||||
try:
|
||||
__import__(module_name)
|
||||
return True
|
||||
except ImportError:
|
||||
return False
|
||||
|
||||
def __str__(self):
|
||||
"""Human-readable system information."""
|
||||
return f"TinyTorch on Python {self.python_version.major}.{self.python_version.minor} ({self.platform} {self.machine})"
|
||||
return f"Python {self.python_version.major}.{self.python_version.minor} on {self.platform} ({self.machine})"
|
||||
|
||||
def is_compatible(self):
|
||||
"""Check if system meets minimum requirements."""
|
||||
return self.python_version >= (3, 8)
|
||||
|
||||
def is_ml_ready(self):
|
||||
"""Check if system is ready for ML development."""
|
||||
return self.is_compatible() and self.has_numpy
|
||||
|
||||
def ml_status_report(self):
|
||||
"""Generate a detailed ML readiness report."""
|
||||
status = []
|
||||
status.append("🔥 TinyTorch System Status")
|
||||
status.append(f"Platform: {self.platform} ({self.machine})")
|
||||
status.append(f"Python: {self.python_version.major}.{self.python_version.minor}.{self.python_version.micro}")
|
||||
|
||||
# Check requirements
|
||||
status.append("\n📋 ML Library Status:")
|
||||
status.append(f" NumPy: {'✅ Available' if self.has_numpy else '❌ Missing'}")
|
||||
status.append(f" PyTorch: {'✅ Available' if self.has_torch else '❌ Missing (optional)'}")
|
||||
status.append(f" TensorFlow: {'✅ Available' if self.has_tensorflow else '❌ Missing (optional)'}")
|
||||
|
||||
# Overall status
|
||||
ready = self.is_ml_ready()
|
||||
status.append(f"\n🎯 Overall Status: {'✅ Ready for TinyTorch!' if ready else '❌ Missing requirements'}")
|
||||
|
||||
if ready:
|
||||
status.append("🚀 You're all set to build an ML framework from scratch!")
|
||||
else:
|
||||
status.append("💡 Install missing libraries: pip install numpy")
|
||||
|
||||
return "\n".join(status)
|
||||
|
||||
# %%
|
||||
# Test the enhanced SystemInfo class
|
||||
print("=== Enhanced System Information ===")
|
||||
# Test the SystemInfo class
|
||||
info = SystemInfo()
|
||||
print(f"System: {info}")
|
||||
print(f"Compatible: {info.is_compatible()}")
|
||||
print(f"ML Ready: {info.is_ml_ready()}")
|
||||
print()
|
||||
print(info.ml_status_report())
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 3: The NBDev Export Process - Your Educational Superpower
|
||||
|
||||
Now let's understand how NBDev transforms your notebook into production code while maintaining the educational experience!
|
||||
|
||||
### 🔄 The Magic of Educational `#| export`
|
||||
|
||||
Every cell marked with `#| export` becomes part of the `tinytorch` package, but NBDev's educational directives control what students see vs what instructors see.
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| code-fold: show
|
||||
print("=== NBDev Educational Export Demonstration ===")
|
||||
print("🎓 Learning Side: You work in modules/setup/setup_dev.ipynb")
|
||||
print("🔧 Building Side: Code exports to tinytorch/core/utils.py")
|
||||
print()
|
||||
print("✨ Educational Directives Used:")
|
||||
print(" #|export - Code goes to package")
|
||||
print(" #|hide - Solutions hidden from students")
|
||||
print(" #|code-fold - Collapsible sections")
|
||||
print(" Single source - One notebook, two audiences")
|
||||
print()
|
||||
print("🚀 Try this export command:")
|
||||
print(" python bin/tito.py sync --module setup")
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
### 🎯 Advanced ML Preview (Instructor Level)
|
||||
|
||||
The following cell demonstrates advanced concepts that will be hidden from beginners but visible to instructors:
|
||||
"""
|
||||
|
||||
# %%
|
||||
#| hide
|
||||
#| filter_stream ImportWarning DeprecationWarning
|
||||
def advanced_ml_preview():
|
||||
"""
|
||||
ADVANCED CONTENT - Hidden from beginners
|
||||
|
||||
This demonstrates how to hide complex implementations
|
||||
while still teaching the concepts progressively.
|
||||
"""
|
||||
import math
|
||||
|
||||
def sigmoid(x):
|
||||
"""Sigmoid activation function - fundamental to neural networks."""
|
||||
return 1 / (1 + math.exp(-x))
|
||||
|
||||
def relu(x):
|
||||
"""ReLU activation function - most common in modern ML."""
|
||||
return max(0, x)
|
||||
|
||||
# Demonstrate activation functions
|
||||
test_vals = [-2, -1, 0, 1, 2]
|
||||
print("🔬 Activation Functions Preview:")
|
||||
for x in test_vals:
|
||||
print(f" x={x:2}: sigmoid={sigmoid(x):.3f}, relu={relu(x):.3f}")
|
||||
|
||||
# Show preview to instructors
|
||||
print("🔬 Advanced ML Preview (hidden from beginners):")
|
||||
advanced_ml_preview()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 4: Testing and Quality Assurance
|
||||
|
||||
Quality code is essential for ML systems. Let's test our implementations!
|
||||
"""
|
||||
|
||||
# %%
|
||||
def run_setup_tests():
|
||||
"""Run comprehensive tests on our setup module functions."""
|
||||
print("=== Running Setup Module Tests ===")
|
||||
|
||||
# Test basic functions
|
||||
assert hello_tinytorch() is not None, "hello_tinytorch should return something"
|
||||
assert len(hello_tinytorch()) > 20, "hello_tinytorch should be educational"
|
||||
assert add_numbers(2, 3) == 5, "Addition should work correctly"
|
||||
|
||||
# Test vector operations
|
||||
v1, v2 = [1.0, 2.0], [3.0, 4.0]
|
||||
assert vector_add(v1, v2) == [4.0, 6.0], "Vector addition should work"
|
||||
assert vector_dot(v1, v2) == 11.0, "Dot product should work"
|
||||
|
||||
# Test system info
|
||||
info = SystemInfo()
|
||||
assert isinstance(info.ml_status_report(), str), "Status report should be string"
|
||||
assert info.is_compatible(), "Should be compatible with Python 3.8+"
|
||||
|
||||
print("✅ All tests passed! Your setup module is working correctly.")
|
||||
print("📚 Ready for production ML systems development!")
|
||||
return True
|
||||
|
||||
# Run the comprehensive tests
|
||||
run_setup_tests()
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 5: Export and Build Process
|
||||
## Step 3: Try the Export Process
|
||||
|
||||
Now let's export our code! In your terminal, run:
|
||||
|
||||
@@ -374,19 +129,18 @@ python bin/tito.py sync --module setup
|
||||
|
||||
This will export the code marked with `#| export` to `tinytorch/core/utils.py`.
|
||||
|
||||
**What happens during educational export:**
|
||||
1. NBDev scans this notebook for `#| export` cells
|
||||
2. **Students see**: Exercise versions with TODOs and hints
|
||||
3. **Instructors see**: Complete solutions with `#|hide` directive
|
||||
4. **Package gets**: The complete implementation (instructor version)
|
||||
5. **Documentation shows**: Educational progression with hide/show buttons
|
||||
**What happens during export:**
|
||||
1. nbdev scans this notebook for `#| export` cells
|
||||
2. Extracts the Python code
|
||||
3. Writes it to `tinytorch/core/utils.py` (because of `#| default_exp core.utils`)
|
||||
4. Handles imports and dependencies automatically
|
||||
|
||||
**🔍 Verification**: After export, check `tinytorch/core/utils.py` - you'll see the complete functions with auto-generated headers!
|
||||
**🔍 Verification**: After export, check `tinytorch/core/utils.py` - you'll see your functions there with auto-generated headers pointing back to this notebook!
|
||||
"""
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## Step 6: Run Tests
|
||||
## Step 4: Run Tests
|
||||
|
||||
After exporting, run the tests:
|
||||
|
||||
@@ -396,7 +150,7 @@ python bin/tito.py test --module setup
|
||||
|
||||
This will run all tests for the setup module and verify your implementation works correctly.
|
||||
|
||||
## Step 7: Check Your Progress
|
||||
## Step 5: Check Your Progress
|
||||
|
||||
See your overall progress:
|
||||
|
||||
@@ -409,42 +163,26 @@ This shows which modules are complete and which are pending.
|
||||
|
||||
# %% [markdown]
|
||||
"""
|
||||
## 🎉 Congratulations! You've Mastered NBDev Educational Features
|
||||
## 🎉 Congratulations!
|
||||
|
||||
### ✨ What You've Accomplished
|
||||
You've learned the TinyTorch development workflow:
|
||||
|
||||
- ✅ **NBDev Educational Directives**: Used `#|hide`, `#|code-fold`, `#|filter_stream`
|
||||
- ✅ **Progressive Learning**: Started simple, built to ML complexity
|
||||
- ✅ **Single Source Truth**: One notebook serves students AND instructors
|
||||
- ✅ **ML-Relevant Code**: Vector operations, activation previews, system checks
|
||||
- ✅ **Quality Assurance**: Comprehensive testing approach
|
||||
- ✅ **Production Workflow**: Export to package with educational metadata
|
||||
1. ✅ Write code in notebooks with `#| export`
|
||||
2. ✅ Export with `tito sync --module setup`
|
||||
3. ✅ Test with `tito test --module setup`
|
||||
4. ✅ Check progress with `tito info`
|
||||
|
||||
### 🔄 The Educational TinyTorch Development Rhythm
|
||||
**This is the rhythm you'll use for every module in TinyTorch.**
|
||||
|
||||
1. **Write** code with educational directives (`#|hide` for solutions)
|
||||
2. **Test** implementations in notebook (both student and instructor versions)
|
||||
3. **Export** with `python bin/tito.py sync --module setup`
|
||||
4. **Verify** with `python bin/tito.py test --module setup`
|
||||
5. **Progress** with `python bin/tito.py info`
|
||||
### Next Steps
|
||||
|
||||
### 🚀 Next Steps: Ready for Real ML Systems
|
||||
Ready for the real work? Head to **Module 1: Tensor** where you'll build the core data structures that power everything else in TinyTorch.
|
||||
|
||||
You're now ready for **Module 1: Tensor** where you'll build the foundation of all ML systems using this same educational pattern!
|
||||
**Development Tips:**
|
||||
- Always test your code in the notebook first
|
||||
- Export frequently to catch issues early
|
||||
- Read error messages carefully - they're designed to help
|
||||
- When stuck, check if your code exports cleanly first
|
||||
|
||||
**What's Coming:**
|
||||
- 📊 **Tensors**: N-dimensional arrays with educational progression
|
||||
- 🔄 **Autograd**: Automatic differentiation with hidden complexity
|
||||
- 🧠 **Networks**: MLPs, CNNs with step-by-step revelation
|
||||
- ⚡ **Training**: End-to-end learning with instructor solutions
|
||||
|
||||
### 💡 Educational Development Tips
|
||||
|
||||
- **Use `#|hide` strategically** - provide complete solutions but let students try first
|
||||
- **Progressive revelation** - start simple, build complexity with fold/hide
|
||||
- **Test both versions** - ensure student stubs and instructor solutions work
|
||||
- **Single source truth** - maintain one notebook, serve two audiences
|
||||
- **Quality first** - educational code should be production-ready
|
||||
|
||||
**Happy building, ML educator! 🔥**
|
||||
Happy building! 🔥
|
||||
"""
|
||||
|
||||
Reference in New Issue
Block a user